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PurposeSpinal muscular atrophy (SMA), caused by loss of the SMN1 gene, is a leading cause of early childhood death. Due to the near identical sequences of SMN1 and SMN2, analysis of this region is challenging. Population-wide SMA screening to quantify the SMN1 copy number (CN) is recommended by the American College of Medical Genetics.MethodsWe developed a method that accurately identifies the CN of SMN1 and SMN2 using genome sequencing (GS) data by analyzing read depth and eight informative reference genome differences between SMN1/2.ResultsWe characterized SMN1/2 in 12,747 genomes, identified 1568 samples with SMN1 gains or losses and 6615 samples with SMN2 gains or losses and calculated a pan-ethnic carrier frequency of 2%, consistent with previous studies. Additionally, 99.8% of our SMN1 and 99.7% of SMN2 CN calls agreed with orthogonal methods, with a recall of 100% for SMA and 97.8% for carriers, and a precision of 100% for both SMA and carriers.ConclusionThis SMN copy number caller can be used to identify both carrier and affected status of SMA, enabling SMA testing to be offered as a comprehensive test in neonatal care and an accurate carrier screening tool in GS sequencing projects.

Summary Intra-tumor heterogeneity (ITH) is a mechanism of therapeutic resistance and therefore an important clinical challenge. However, the extent, origin, and drivers of ITH across cancer types are poorly understood. To address this, we extensively characterize ITH across whole-genome sequences of 2,658 cancer samples spanning 38 cancer types. Nearly all informative samples (95.1%) contain evidence of distinct subclonal expansions with frequent branching relationships between subclones. We observe positive selection of subclonal driver mutations across most cancer types and identify cancer type-specific subclonal patterns of driver gene mutations, fusions, structural variants, and copy number alterations as well as dynamic changes in mutational processes between subclonal expansions. Our results underline the importance of ITH and its drivers in tumor evolution and provide a pan-cancer resource of comprehensively annotated subclonal events from whole-genome sequencing data., Graphical abstract, Highlights • Pan-cancer resource of comprehensively annotated intra-tumor heterogeneity (ITH) • ITH is pervasive across cancers and shows cancer type-specific patterns • Branching phylogenies are common • Dynamic changes in mutational processes between subclonal expansions, Dentro et al. provide a comprehensive annotation of intra-tumor heterogeneity and its drivers in cancer evolution.

PURPOSE: Spinal muscular atrophy (SMA), caused by loss of the SMN1 gene, is a leading cause of early childhood death. Due to the near identical sequences of SMN1 and SMN2, analysis of this region is challenging. Population-wide SMA screening to quantify the SMN1 copy number (CN) is recommended by the American College of Medical Genetics and Genomics. METHODS: We developed a method that accurately identifies the CN of SMN1 and SMN2 using genome sequencing (GS) data by analyzing read depth and eight informative reference genome differences between SMN1/2. RESULTS: We characterized SMN1/2 in 12,747 genomes, identified 1568 samples with SMN1 gains or losses and 6615 samples with SMN2 gains or losses, and calculated a pan-ethnic carrier frequency of 2%, consistent with previous studies. Additionally, 99.8% of our SMN1 and 99.7% of SMN2 CN calls agreed with orthogonal methods, with a recall of 100% for SMA and 97.8% for carriers, and a precision of 100% for both SMA and carriers. CONCLUSION: This SMN copy-number caller can be used to identify both carrier and affected status of SMA, enabling SMA testing to be offered as a comprehensive test in neonatal care and an accurate carrier screening tool in GS sequencing projects.

Background Angiomatous and microcytic meningiomas are classified as rare subtypes of grade I meningiomas by World Health Organization (WHO). They typically exhibit distinct histopathological features as indicated by their WHO titles; however, these angiomatous and microcystic features are often intermixed. Recently, angiomatous meningiomas were reported to show characteristic chromosomal polysomies unlike the other WHO grade I meningiomas. In the present study, we hypothesize that microcystic meningiomas share similar cytogenetic abnormalities with angiomatous meningioma. Methods We performed copy number analysis using single nucleotide polymorphism (SNP) arrays for three angiomatous and eight microcystic meningiomas. Of these, three angiomatous and three microcystic meningiomas were also analyzed by whole exome sequencing and RNA sequencing. Results We first analyzed three angiomatous and three microcystic meningiomas for which both frozen tissues and peripheral blood were accessible. Copy number analysis confirmed previously reported multiple polysomies in angiomatous meningiomas, which were entirely replicated in microcystic meningiomas when analyzed on different analytical platforms with five additional samples prepared from formalin-fixed paraffin-embedded tumors. Polysomy of chromosome 5 was found in all cases, along with chromosome 6, 12, 17, 18, and 20 in more than half of the cases including both angiomatous and microcystic meningiomas. Furthermore, next generation sequencing did not reveal any distinctive somatic point mutations or differences in gene expression characterizing either angiomatous or microcystic meningiomas, indicating a common genetic mechanism underlying tumorigenesis. Conclusions Angiomatous and microcystic meningiomas have substantially similar genetic profiles represented by the characteristic patterns of multiple polysomies originating from chromosome 5 amplification.

Current cell-free DNA (cfDNA) next generation sequencing (NGS) precision oncology workflows are typically limited to targeted and/or disease-specific applications. In advanced cancer, disease burden and cfDNA tumor content are often elevated, yielding unique precision oncology opportunities. We sought to demonstrate the utility of a pan-cancer, rapid, inexpensive, whole genome NGS of cfDNA approach (PRINCe) as a precision oncology screening strategy via ultra-low coverage (~0.01x) tumor content determination through genome-wide copy number alteration (CNA) profiling. We applied PRINCe to a retrospective cohort of 124 cfDNA samples from 100 patients with advanced cancers, including 76 men with metastatic castration-resistant prostate cancer (mCRPC), enabling cfDNA tumor content approximation and actionable focal CNA detection, while facilitating concordance analyses between cfDNA and tissue-based NGS profiles and assessment of cfDNA alteration associations with mCRPC treatment outcomes. Therapeutically relevant focal CNAs were present in 42 (34%) cfDNA samples, including 36 of 93 (39%) mCRPC patient samples harboring AR amplification. PRINCe identified pre-treatment cfDNA CNA profiles facilitating disease monitoring. Combining PRINCe with routine targeted NGS of cfDNA enabled mutation and CNA assessment with coverages tuned to cfDNA tumor content. In mCRPC, genome-wide PRINCe cfDNA and matched tissue CNA profiles showed high concordance (median Pearson correlation = 0.87), and PRINCe detectable AR amplifications predicted reduced time on therapy, independent of therapy type (Kaplan-Meier log-rank test, chi-square = 24.9, p < 0.0001). Our screening approach enables robust, broadly applicable cfDNA-based precision oncology for patients with advanced cancer through scalable identification of therapeutically relevant CNAs and pre-/post-treatment genomic profiles, enabling cfDNA- or tissue-based precision oncology workflow optimization.

Genetic abnormalities/variations contribute to the most of human diseases including cancer and congenital disorders. Advances in tools for human genome analysis, such as genomic arrays and next-generation sequences, based on the information of human genome sequences provided a great opportunity to identify novel causative genes and disease-associated genes through whole exploration of disease-specific abnormalities in the sequence and copy-number levels. Novel target genes for cancer diagnosis and therapy could be identified through combined structural and functional approach for cancer genome using whole genome and epigenome scanning by array and sequencer as well as functional and expression analyses of candidate genes, resulting in the development of novel diagnostic and therapeutic methods useful for the ‘ personalized medicine’ .